Methods and systems for causative chaining of prognostic label classifications

ABSTRACT

A system for causative chaining of prognostic label classifications includes a classification device configured to receive training data including a plurality of first data entries, each including at least a first element of physiological state data and at least a correlated first prognostic label and a plurality of second data entries, each including at least a second prognostic label and at least a correlated third prognostic label, and to record at least a first biological extraction. The system includes a prognostic label learner configured to generate at least a first prognostic output as a function of the first training set and the at least a physiological test sample, and a causal link learner configured to generate at least a second prognostic output causally linked to the first prognostic output as a function of the second training set and the at least a first prognostic output.

RELATED APPLICATION DATA

This application is a continuation-in-part U.S. patent application Ser.No. 16/430,387, filed on Jun. 3, 2019 which is hereby incorporated byreference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of machinelearning. In particular, the present invention is directed to methodsand systems for causative chaining of prognostic label classifications.

BACKGROUND

Automated analysis of physiological data can be highly challenging dueto the multiplicity of types and sources of data to be analyzed, whichin turn is a reflection of the immense complexity of systems sorepresented. Burgeoning knowledge concerning microscopic and macroscopicphysiological states, and concomitantly expanding modes of detection andanalysis of the same, have further exacerbated this problem.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for causative chaining of prognostic labelclassifications includes at least a computing device, the computingdevice designed and configured to receive training data, whereinreceiving the training data further comprises receiving a first trainingset including a plurality of first data entries, each first data entryof the plurality of first data entries including at least a firstelement of physiological state data and at least a correlated firstprognostic label, and receiving a second training set including aplurality of second data entries, each second data entry of theplurality of second data entries including at least a second prognosticlabel and at least a correlated third prognostic label, and record atleast a first biological extraction. The system includes a prognosticlabel learner operating on the at least a computing device, theprognostic label learner designed and configured to generate at least afirst prognostic output as a function of the first training set and theat least a physiological test sample, wherein the prognostic labellearner is further configured to generate a third prognostic output as afunction of the first training set and the at least a second biologicalextraction, and a causal link learner operating on the at least acomputing device, the causal link learner designed and configured togenerate at least a second prognostic output as a function of the secondtraining set and the at least a first prognostic output, wherein the atleast a second prognostic output represents a cause of the at least afirst prognostic output, and wherein the at least a second prognosticoutput further comprises a plurality of second prognostic outputs. Theat least a computing device is configured to determine that the at leasta second prognostic output includes a fundamental prognostic label.

In another aspect, a method of causative chaining of prognostic labelclassifications includes receiving, by at least a computing device,training data, wherein receiving the training data further includesreceiving a first training set including a plurality of first dataentries, each first data entry of the plurality of first data entriesincluding at least a first element of physiological state data and atleast a correlated first prognostic label, and receiving a secondtraining set including a plurality of second data entries, each seconddata entry of the plurality of second data entries including at least asecond prognostic label and at least a correlated third prognosticlabel. The method includes recording, by the at least a computingdevice, at least a first biological extraction. The method includesgenerating, by the computing device, at least a first prognostic outputas a function of the first training set and the at least a physiologicaltest sample. The method includes generating, by the at least a computingdevice, at least a second prognostic output as a function of the secondtraining set and the at least a first prognostic output, wherein the atleast a second prognostic output represents a cause of the at least afirst prognostic output. The method includes determining that the atleast a second prognostic output includes a fundamental prognosticlabel.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for causative chaining of prognostic label classifications;

FIG. 2 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 3 is a block diagram illustrating an exemplary embodiment of aphysiological sample database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of aprognostic label database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of aprognostic label learner and associated system elements;

FIG. 7 is a block diagram illustrating an exemplary embodiment of anameliorative process label learner and associated system elements;

FIG. 8 illustrates flow diagram illustrating an exemplary embodiment ofa method of causative chaining of prognostic label classifications;

FIG. 9 illustrates flow diagram illustrating an exemplary embodiment ofa method of causative chaining of prognostic label classifications; and

FIG. 10 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Embodiments of systems and methods disclosed herein may classifyphysiological samples to one or more prognostic labels using trainingsets correlating physiological state data to prognostic labels;prognostic labels are further linked to causally related prognosticlabels via additional machine-learning processes. Categorization of dataelements in training sets may be accomplished using unsupervisedclustering algorithms; categorization may alternatively or additionallyinvolve expert data inputs provided by graphical user interface entriesor extracted using language processing algorithms from a corpus ofsubject-specific documents.

Referring now to FIG. 1, an exemplary embodiment of a system 100 forcausative chaining of prognostic label classifications is illustrated.System includes a classification device 104. Classification device 104may include any computing device as described in this disclosure,including without limitation a microcontroller, microprocessor, digitalsignal processor (DSP) and/or system on a chip (SoC) as described inthis disclosure. Classification device 104 may be housed with, may beincorporated in, or may incorporate one or more sensors of at least asensor. Computing device may include, be included in, and/or communicatewith a mobile device such as a mobile telephone or smartphone.Classification device 104 may include a single computing deviceoperating independently, or may include two or more computing deviceoperating in concert, in parallel, sequentially or the like; two or morecomputing devices may be included together in a single computing deviceor in two or more computing devices. Classification device 104 with oneor more additional devices as described below in further detail via anetwork interface device. Network interface device may be utilized forconnecting a classification device 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. Classification device 104 mayinclude but is not limited to, for example, a classification device 104or cluster of computing devices in a first location and a secondcomputing device or cluster of computing devices in a second location.Classification device 104 may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Classification device 104 may distribute one ormore computing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. Classification device 104 may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofsystem 100 and/or computing device.

Still referring to FIG. 1, classification device 104 and/or one or moremodules operating thereon may be designed and/or configured to performany method, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, classification device 104 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Classification device104 may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing.

Continuing to refer to FIG. 1, classification device 104 may be designedand configured to receive training data. Training data, as used herein,is data containing correlation that a machine-learning process may useto model relationships between two or more categories of data elements.For instance, and without limitation, training data may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data may be formatted and/or organizedby categories of data elements, for instance by associating dataelements with one or more descriptors corresponding to categories ofdata elements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

Still referring to FIG. 1, categorization device may be configured toreceive a first training set 108 including a plurality of first dataentries, each first data entry of the first training set 108 includingat least an element of physiological state data and at least acorrelated first prognostic label 116. At least an element ofphysiological state data may include any data indicative of a person'sphysiological state; physiological state may be evaluated with regard toone or more measures of health of a person's body, one or more systemswithin a person's body such as a circulatory system, a digestive system,a nervous system, or the like, one or more organs within a person'sbody, and/or any other subdivision of a person's body useful fordiagnostic or prognostic purposes. Physiological state data may include,without limitation, hematological data, such as red blood cell count,which may include a total number of red blood cells in a person's bloodand/or in a blood sample, hemoglobin levels, hematocrit representing apercentage of blood in a person and/or sample that is composed of redblood cells, mean corpuscular volume, which may be an estimate of theaverage red blood cell size, mean corpuscular hemoglobin, which maymeasure average weight of hemoglobin per red blood cell, meancorpuscular hemoglobin concentration, which may measure an averageconcentration of hemoglobin in red blood cells, platelet count, meanplatelet volume which may measure the average size of platelets, redblood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1, physiological state data may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data may include, without limitation, one ormore measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data may include measuresof estimated glomerular filtration rate (eGFR). Physiological state datamay include quantities of C-reactive protein, estradiol, ferritin,folate, homocysteine, prostate-specific Ag, thyroid-stimulating hormone,vitamin D, 25 hydroxy, blood urea nitrogen, creatinine, sodium,potassium, chloride, carbon dioxide, uric acid, albumin, globulin,calcium, phosphorus, alkaline photophatase, alanine amino transferase,aspartate amino transferase, lactate dehydrogenase (LDH), bilirubin,gamma-glutamyl transferase (GGT), iron, and/or total iron bindingcapacity (TIBC), or the like. Physiological state data may includeantinuclear antibody levels. Physiological state data may includealuminum levels. Physiological state data may include arsenic levels.Physiological state data may include levels of fibronigen, plasmacystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1, physiological state data may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data may include a measurement blood pressure, including withoutlimitation systolic and diastolic blood pressure. Physiological statedata may include a measure of waist circumference. Physiological statedata may include body mass index (BMI). Physiological state data mayinclude one or more measures of bone mass and/or density such asdual-energy x-ray absorptiometry. Physiological state data may includeone or more measures of muscle mass. Physiological state data mayinclude one or more measures of physical capability such as withoutlimitation measures of grip strength, evaluations of standing balance,evaluations of gait speed, pegboard tests, timed up and go tests, and/orchair rising tests.

Still viewing FIG. 1, physiological state data may include one or moremeasures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state datamay include one or more evaluations of sensory ability, includingmeasures of audition, vision, olfaction, gustation, vestibular functionand pain.

Continuing to refer to FIG. 1, physiological state data may includepsychological data. Psychological data may include any data generatedusing psychological, neuro-psychological, and/or cognitive evaluations,as well as diagnostic screening tests, personality tests, personalcompatibility tests, or the like; such data may include, withoutlimitation, numerical score data entered by an evaluating professionaland/or by a subject performing a self-test such as a computerizedquestionnaire. Psychological data may include textual, video, or imagedata describing testing, analysis, and/or conclusions entered by amedical professional such as without limitation a psychologist,psychiatrist, psychotherapist, social worker, a medical doctor, or thelike. Psychological data may include data gathered from userinteractions with persons, documents, and/or computing devices; forinstance, user patterns of purchases, including electronic purchases,communication such as via chat-rooms or the like, any textual, image,video, and/or data produced by the subject, any textual image, videoand/or other data depicting and/or describing the subject, or the like.Any psychological data and/or data used to generate psychological datamay be analyzed using machine-learning and/or language processingmodules as described in this disclosure.

Still referring to FIG. 1, physiological state data may include genomicdata, including deoxyribonucleic acid (DNA) samples and/or sequences,such as without limitation DNA sequences contained in one or morechromosomes in human cells. Genomic data may include, withoutlimitation, ribonucleic acid (RNA) samples and/or sequences, such assamples and/or sequences of messenger RNA (mRNA) or the like taken fromhuman cells. Genetic data may include telomere lengths. Genomic data mayinclude epigenetic data including data describing one or more states ofmethylation of genetic material. Physiological state data may includeproteomic data, which as used herein is data describing all proteinsproduced and/or modified by an organism, colony of organisms, or systemof organisms, and/or a subset thereof. Physiological state data mayinclude data concerning a microbiome of a person, which as used hereinincludes any data describing any microorganism and/or combination ofmicroorganisms living on or within a person, including withoutlimitation biomarkers, genomic data, proteomic data, and/or any othermetabolic or biochemical data useful for analysis of the effect of suchmicroorganisms on other physiological state data of a person, and/or onprognostic labels and/or ameliorative processes as described in furtherdetail below.

With continuing reference to FIG. 1, physiological state data mayinclude one or more user-entered descriptions of a person'sphysiological state. One or more user-entered descriptions may include,without limitation, user descriptions of symptoms, which may includewithout limitation current or past physical, psychological, perceptual,and/or neurological symptoms, user descriptions of current or pastphysical, emotional, and/or psychological problems and/or concerns, userdescriptions of past or current treatments, including therapies,nutritional regimens, exercise regimens, pharmaceuticals or the like, orany other user-entered data that a user may provide to a medicalprofessional when seeking treatment and/or evaluation, and/or inresponse to medical intake papers, questionnaires, questions frommedical professionals, or the like. Physiological state data may includeany physiological state data, as described above, describing anymulticellular organism living in or on a person including any parasiticand/or symbiotic organisms living in or on the persons; non-limitingexamples may include mites, nematodes, flatworms, or the like. Examplesof physiological state data described in this disclosure are presentedfor illustrative purposes only and are not meant to be exhaustive.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples ofphysiological state data that may be used consistently with descriptionsof systems and methods as provided in this disclosure.

Continuing to refer to FIG. 1, each element of first training set 108includes at least a first prognostic label 116. A prognostic label, asdescribed herein, is an element of data identifying and/or describing acurrent, incipient, or probable future medical condition affecting aperson; medical condition may include a particular disease, one or moresymptoms associated with a syndrome, a syndrome, and/or any othermeasure of current or future health and/or healthy aging. At least aprognostic label may be associated with a physical and/or somaticcondition, a mental condition such as a mental illness, neurosis, or thelike, or any other condition affecting human health that may beassociated with one or more elements of physiological state data asdescribed in further detail below. Conditions associated with prognosticlabels may include, without limitation one or more diseases, defined forpurposes herein as conditions that negatively affect structure and/orfunction of part or all of an organism. Conditions associated withprognostic labels may include, without limitation, acute or chronicinfections, including without limitation infections by bacteria,archaea, viruses, viroids, prions, single-celled eukaryotic organismssuch as amoeba, paramecia, trypanosomes, plasmodia, leishmania, and/orfungi, and/or multicellular parasites such as nematodes, arthropods,fungi, or the like. Prognostic labels may be associated with one or moreimmune disorders, including without limitation immunodeficiencies and/orauto-immune conditions. Prognostic labels may be associated with one ormore metabolic disorders. Prognostic labels may be associated with oneor more endocrinal disorders. Prognostic labels may be associated withone or more cardiovascular disorders. Prognostic labels may beassociated with one or more respiratory disorders. Prognostic labels maybe associated with one or more disorders affecting connective tissue.Prognostic labels may be associated with one or more digestivedisorders. Prognostic labels may be associated with one or moreneurological disorders such as neuromuscular disorders, dementia, or thelike. Prognostic labels may be associated with one or more disorders ofthe excretory system, including without limitation nephrologicaldisorders. Prognostic labels may be associated with one or more liverdisorders. Prognostic labels may be associated with one or moredisorders of the bones such as osteoporosis. Prognostic labels may beassociated with one or more disorders affecting joints, such asosteoarthritis, gout, and/or rheumatoid arthritis. Prognostic labels beassociated with one or more cancers, including without limitationcarcinomas, lymphomas, leukemias, germ cell tumor cancers, blastomas,and/or sarcomas. Prognostic labels may include descriptors of latent,dormant, and/or apparent disorders, diseases, and/or conditions.Prognostic labels may include descriptors of conditions for which aperson may have a higher than average probability of development, suchas a condition for which a person may have a “risk factor”; forinstance, a person currently suffering from abdominal obesity may have ahigher than average probability of developing type II diabetes.Prognostic labels may include descriptors of current, incipient, past,and/or potential future psychological and/or neurological conditions.The above-described examples are presented for illustrative purposesonly and are not intended to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of conditions that may be associated with prognosticlabels as described in this disclosure.

Still referring to FIG. 1, at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as without limitation the International StatisticalClassification of Diseases and Related Health Problems (ICD), theDiagnostic and Statistical Manual of Mental Disorders (DSM 5), or thelike. In general, there is no limitation on forms textual data ornon-textual data used as at least a prognostic label may take; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various forms which may be suitable for use as at least aprognostic label consistently with this disclosure.

With continued reference to FIG. 1, in each first data element of firsttraining set 108, at least a first prognostic label 116 of the dataelement is correlated with at least an element of physiological statedata of the data element. In an embodiment, a first element ofphysiological state data 112 is correlated with at least a firstprognostic label 116 where the first element of physiological state data112 is located in the same data element and/or portion of data elementas the at least a first prognostic label 116; for example, and withoutlimitation, a first element of physiological state data 112 iscorrelated with a prognostic element where both first element ofphysiological state data 112 and prognostic element are contained withinthe same first data element of the first training set 108. As a furtherexample, a first element of physiological state data 112 is correlatedwith a prognostic element where both share a category label as describedin further detail below, where each is within a certain distance of theother within an ordered collection of data in data element, or the like.Still further, a first element of physiological state data 112 may becorrelated with at least a first prognostic label 116 where the firstelement of physiological state data 112 and the at least a firstprognostic label 116 share an origin, such as being data that wascollected with regard to a single person or the like. In an embodiment,a first datum may be more closely correlated with a second datum in thesame data element than with a third datum contained in the same dataelement; for instance, the first element and the second element may becloser to each other in an ordered set of data than either is to thethird element, the first element and second element may be contained inthe same subdivision and/or section of data while the third element isin a different subdivision and/or section of data, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various forms and/or degrees of correlation betweenphysiological data and prognostic labels that may exist in firsttraining set 108 and/or first data element consistently with thisdisclosure.

In an embodiment, and still referring to FIG. 1, classification device104 may be designed and configured to associate at least an element ofphysiological state data with at least a category from a list ofsignificant categories of physiological state data. Significantcategories of physiological state data may include labels and/ordescriptors describing types of physiological state data that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data based on degree ofdiagnostic relevance to one or more impactful conditions and/or withinone or more medical or public health fields. For instance, and withoutlimitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, and/or HDL-C may be recognized as useful in identifyingconditions such as poor thyroid function, insulin resistance, bloodglucose dysregulation, magnesium deficiency, dehydration, kidneydisease, familial hypercholesterolemia, liver dysfunction, oxidativestress, inflammation, malabsorption, anemia, alcohol abuse, diabetes,hypercholesterolemia, coronary artery disease, atherosclerosis, or thelike. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional categories ofphysiological data that may be used consistently with this disclosure.

Still referring to FIG. 1, classification device 104 may receive thelist of significant categories according to any suitable process; forinstance, and without limitation, classification device 104 may receivethe list of significant categories from at least an expert. In anembodiment, classification device 104 and/or a user device connected toclassification device 104 may provide a graphical user interface 120,which may include without limitation a form or other graphical elementhaving data entry fields, wherein one or more experts, including withoutlimitation clinical and/or scientific experts, may enter informationdescribing one or more categories of physiological data that the expertsconsider to be significant or useful for detection of conditions; fieldsin graphical user interface 120 may provide options describingpreviously identified categories, which may include a comprehensive ornear-comprehensive list of types of physiological data detectable usingknown or recorded testing methods, for instance in “drop-down” lists,where experts may be able to select one or more entries to indicatetheir usefulness and/or significance in the opinion of the experts.Fields may include free-form entry fields such as text-entry fieldswhere an expert may be able to type or otherwise enter text, enablingexpert to propose or suggest categories not currently recorded.Graphical user interface 120 or the like may include fieldscorresponding to prognostic labels, where experts may enter datadescribing prognostic labels and/or categories of prognostic labels theexperts consider related to entered categories of physiological data;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded prognosticlabels, and which may be comprehensive, permitting each expert to selecta prognostic label and/or a plurality of prognostic labels the expertbelieves to be predicted and/or associated with each category ofphysiological data selected by the expert. Fields for entry ofprognostic labels and/or categories of prognostic labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface 120 may provide an expert with a field in which toindicate a reference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

Referring again to FIG. 1, data information describing significantcategories of physiological data, relationships of such categories toprognostic labels, and/or significant categories of prognostic labelsmay alternatively or additionally be extracted from one or moredocuments using a language processing module 124. Language processingmodule 124 may include any hardware and/or software module. Languageprocessing module 124 may be configured to extract, from the one or moredocuments, one or more words. One or more words may include, withoutlimitation, strings of one or characters, including without limitationany sequence or sequences of letters, numbers, punctuation, diacriticmarks, engineering symbols, geometric dimensioning and tolerancing(GD&T) symbols, chemical symbols and formulas, spaces, whitespace, andother symbols, including any symbols usable as textual data as describedabove. Textual data may be parsed into tokens, which may include asimple word (sequence of letters separated by whitespace) or moregenerally a sequence of characters as described previously. The term“token,” as used herein, refers to any smaller, individual groupings oftext from a larger source of text; tokens may be broken up by word, pairof words, sentence, or other delimitation. These tokens may in turn beparsed in various ways. Textual data may be parsed into words orsequences of words, which may be considered words as well. Textual datamay be parsed into “n-grams”, where all sequences of n consecutivecharacters are considered. Any or all possible sequences of tokens orwords may be stored as “chains”, for example for use as a Markov chainor Hidden Markov Model.

Still referring to FIG. 1, language processing module 124 may compareextracted words to categories of physiological data recorded atclassification device 104, one or more prognostic labels recorded atclassification device 104, and/or one or more categories of prognosticlabels recorded at classification device 104; such data for comparisonmay be entered on classification device 104 as described above usingexpert data inputs or the like. In an embodiment, one or more categoriesmay be enumerated, to find total count of mentions in such documents.Alternatively or additionally, language processing module 124 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by classificationdevice 104 and/or language processing module 124 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words, categories of physiological data, relationshipsof such categories to prognostic labels, and/or categories of prognosticlabels may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of physiological data, a given relationship of such categoriesto prognostic labels, and/or a given category of prognostic labels. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of physiological data, relationship of such category toprognostic labels, and/or category of prognostic labels is or is notsignificant. For instance, and without limitation, a negative indicationmay be determined from a phrase such as “telomere length was not foundto be an accurate predictor of overall longevity,” whereas a positiveindication may be determined from a phrase such as “telomere length wasfound to be an accurate predictor of dementia,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat classification device 104, or the like.

Still referring to FIG. 1, language processing module 124 and/orclassification device 104 may generate the language processing model byany suitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used hereinare statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 124may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module 124 may use acorpus of documents to generate associations between language elementsin a language processing module 124, and classification device 104 maythen use such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. In an embodiment, classification device 104 mayperform this analysis using a selected set of significant documents,such as documents identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface 120 as described abovein reference to FIG. 9, or may communicate identities of significantdocuments according to any other suitable method of electroniccommunication, or by providing such identity to other persons who mayenter such identifications into classification device 104. Documents maybe entered into classification device 104 by being uploaded by an expertor other persons using, without limitation, file transfer protocol (FTP)or other suitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, classification device 104 may automatically obtain thedocument using such an identifier, for instance by submitting a requestto a database or compendium of documents such as JSTOR as provided byIthaka Harbors, Inc. of New York.

Continuing to refer to FIG. 1, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface 120, alternativesubmission means, and/or extracted from a document or body of documentsas described above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of physiological sample, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 1, classification device 104 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 1, in an embodiment, classification device104 may be configured, for instance as part of receiving the firsttraining set 108, to associate at least correlated first prognosticlabel 116 with at least a category from a list of significant categoriesof prognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult classification device 104 may modify list of significantcategories to reflect this difference.

Still referring to FIG. 1, classification device 104 is designed andconfigured to receive a second training set 128 including a plurality ofsecond data entries. Each second data entry of the second training set128 includes at least a second prognostic label 132; at least a secondprognostic label 132 may include any label suitable for use as at leasta first prognostic label 116 as described above. Each data entry of thesecond training set 128 includes at least a correlated third prognosticlabel, where at least a third prognostic label 136 may include any labelsuitable for use as at least a first prognostic label 116 as describedabove; at least a third prognostic label 136 may be correlated with atleast a second prognostic label 132 in any way described above forcorrelation of at least an element of physiological state data to atleast a first prognostic label 116 as described above.

Continuing to refer to FIG. 1, in an embodiment classification device104 may be configured, for instance as part of receiving second trainingset 128, to associate the at least second prognostic label and/or atleast a third prognostic label 136 with at least a category from a listof significant categories of prognostic labels. This may be performed asdescribed above for use of lists of significant categories with regardto at least a first prognostic label 116. Significance may bedetermined, and/or association with at least a category, may beperformed for prognostic labels in first training set 108 according to afirst process as described above and for prognostic labels in secondtraining set 128 according to a second process as described above.

In an embodiment, and still referring to FIG. 1, classification device104 may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. Classification device 104 may be configured, forinstance as part of receiving second training set 128, to receive atleast a document describing at least a medical history and extract atleast a second data entry of plurality of second data entries from theat least a document. A medical history document may include, forinstance, a document received from an expert and/or medical practitionerdescribing treatment of a patient; document may be anonymized by removalof one or more patient-identifying features from document. A medicalhistory document may include a case study, such as a case studypublished in a medical journal or written up by an expert. A medicalhistory document may contain data describing and/or described by aprognostic label; for instance, the medical history document may list adiagnosis that a medical practitioner made concerning the patient, afinding that the patient is at risk for a given condition and/or evincessome precursor state for the condition, or the like. A medical historydocument may contain data describing and/or described by an additionalprognostic label; for instance, the medical history document may list anunderlying cause of a condition described by another prognostic label, aprognostic label associated with a co-related or simultaneouslydiscovered condition and/or symptom, or the like. For instance, andwithout limitation, a medical professional may report that a firstcondition, associated with a second prognostic label, is caused whollyor in part by a second condition, associated with a third prognosticlabel; as a non-limiting illustration, a patient presenting with type IIdiabetes may also present with obesity, and a medical professional mayenter a report describing the obesity as a cause of the type IIdiabetes. A medical history document may describe an outcome; forinstance, medical history document may describe an improvement in acondition describing or described by a prognostic label, and/or maydescribe that the condition did not improve. For instance, and forillustrative purposes only, a patient presenting with type II diabetesand obesity may be described as losing weight, and a subsequentalleviation and/or cessation in diabetic symptoms may be observed anddescribed in a medical report; a medical professional may further entera conclusion, based on such observation, that obesity was a likelyunderlying cause of type II diabetes. Prognostic labels, correlationsbetween prognostic labels, and/or causative relationships betweenprognostic labels, may be extracted from and/or determined from one ormore medical history documents using any processes for languageprocessing as described above; for instance, language processing module124 may perform such processes. As a non-limiting example, positiveand/or negative indications regarding prognostic labels and/orrelationships therebetween as identified in medical history documentsmay be determined in a manner described above for determination ofpositive and/or negative indications regarding categories ofphysiological data, relationships of such categories to prognosticlabels, and/or categories of prognostic labels.

With continued reference to FIG. 1, second training set 128 may includeat least a data entry including at least a second element ofphysiological state data 140 and at least a correlated fourth prognosticlabel; the at least a data entry including at least a second element ofphysiological state data 140 and at least a correlated fourth prognosticlabel may include any data entry suitable for use as first data entriesin first training set 108 as described above, and may be generatedand/or received by any process suitable for generation and/or receptionof first data entries in first training set 108. In an embodiment,inclusion in second training set 128 of at least a data entry includingat least a second element of physiological state data 140 and at least acorrelated fourth prognostic label permits determination of causalrelationships between a prognostic label in combination with one or moreelements of physiological data with another prognostic label, asdescribed in further detail below.

In an embodiment, and still referring to FIG. 1, data entries in secondtraining set 128 may be labeled and/or selected to indicate causalrelationships; for instance, language processing module 124 maydetermine that an expert textual submission, medical report, or the likedescribes a causal relationship between a first prognostic label 116 anda second prognostic label, and may flag or otherwise indicate such arelationship in an entry in second training set 128. As a furthernon-limiting example, an expert may enter, in a graphical user interface120 as described above, information indicative of a causal relationshipbetween a first prognostic label 116 and a second prognostic label. Sucha causal relationship may be stored in a causal link table of aprognostic label database as described in further detail below.

With continued reference to FIG. 1, classification device 104 may beconfigured, for instance as part of receiving second training set 128,to receiving at least a second data entry of the plurality of seconddata entries from at least an expert. This may be performed, withoutlimitation, using second graphical user interface 120 as describedabove.

Referring now to FIG. 2, data incorporated in first training set 108and/or second training set 128 may be incorporated in one or moredatabases. As a non-limiting example, one or elements of physiologicalstate data may be stored in and/or retrieved from a physiological sampledatabase 200. A physiological sample database 200 may include any datastructure for ordered storage and retrieval of data, which may beimplemented as a hardware or software module. A physiological sampledatabase 200 may be implemented, without limitation, as a relationaldatabase, a key-value retrieval datastore such as a NOSQL database, orany other format or structure for use as a datastore that a personskilled in the art would recognize as suitable upon review of theentirety of this disclosure. A physiological sample database 200 mayinclude a plurality of data entries and/or records corresponding toelements of physiological data as described above. Data entries and/orrecords may describe, without limitation, data concerning particularphysiological samples that have been collected; entries may describereasons for collection of samples, such as without limitation one ormore conditions being tested for, which may be listed with relatedprognostic labels. Data entries may include prognostic labels and/orother descriptive entries describing results of evaluation of pastphysiological samples, including diagnoses that were associated withsuch samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by system 100 in previous iterations of methods,with or without validation of correctness by medical professionals. Dataentries in a physiological sample database 200 may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database; one or moreadditional elements of information may include data associating aphysiological sample and/or a person from whom a physiological samplewas extracted or received with one or more cohorts, includingdemographic groupings such as ethnicity, sex, age, income, geographicalregion, or the like, one or more common diagnoses or physiologicalattributes shared with other persons having physiological samplesreflected in other data entries, or the like. Additional elements ofinformation may include one or more categories of physiological data asdescribed above. Additional elements of information may includedescriptions of particular methods used to obtain physiological samples,such as without limitation physical extraction of blood samples or thelike, capture of data with one or more sensors, and/or any otherinformation concerning provenance and/or history of data acquisition.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in aphysiological sample database 200 may reflect categories, cohorts,and/or populations of data consistently with this disclosure.

Referring now to FIG. 3, one or more database tables in physiologicalsample database 200 may include, as a non-limiting example, a prognosticlink table 300. Prognostic link table 300 may be a table relatingphysiological sample data as described above to prognostic labels; forinstance, where an expert has entered data relating a prognostic labelto a category of physiological sample data and/or to an element ofphysiological sample data via first graphical user interface 120 asdescribed above, one or more rows recording such an entry may beinserted in prognostic link table 300. Alternatively or additionally,linking of prognostic labels to physiological sample data may beperformed entirely in a prognostic label database as described below.

With continued reference to FIG. 3, physiological sample database 200may include tables listing one or more samples according to samplesource. For instance, and without limitation, physiological sampledatabase 200 may include a fluid sample table 304 listing samplesacquired from a person by extraction of fluids, such as withoutlimitation blood, lymph cerebrospinal fluid, or the like. As anothernon-limiting example, physiological sample database 200 may include asensor data table 308, which may list samples acquired using one or moresensors, for instance as described in further detail below. As a furthernon-limiting example, physiological sample database 200 may include agenetic sample table 312, which may list partial or entire sequences ofgenetic material. Genetic material may be extracted and amplified, as anon-limiting example, using polymerase chain reactions (PCR) or thelike. As a further example, also non-limiting, physiological sampledatabase 200 may include a medical report table 316, which may listtextual descriptions of medical tests, including without limitationradiological tests or tests of strength and/or dexterity or the like.Data in medical report table may be sorted and/or categorized using alanguage processing module 124 312, for instance, translating a textualdescription into a numerical value and a label corresponding to acategory of physiological data; this may be performed using any languageprocessing algorithm or algorithms as referred to in this disclosure. Asanother non-limiting example, physiological sample database 200 mayinclude a tissue sample table 320, which may record physiologicalsamples obtained using tissue samples. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in physiologicalsample database 200 consistently with this disclosure.

Referring again to FIG. 2, classification device 104 and/or anotherdevice in system 100 may populate one or more fields in physiologicalsample database 200 using expert information, which may be extracted orretrieved from an expert knowledge database 204. An expert knowledgedatabase 204 may include any data structure and/or data store suitablefor use as a physiological sample database 200 as described above.Expert knowledge database 204 may include data entries reflecting one ormore expert submissions of data such as may have been submittedaccording to any process described above in reference to FIG. 1,including without limitation by using first graphical user interface 120and/or second graphical user interface 120. Expert knowledge databasemay include one or more fields generated by language processing module124, such as without limitation fields extracted from one or moredocuments as described above. For instance, and without limitation, oneor more categories of physiological data and/or related prognosticlabels and/or categories of prognostic labels associated with an elementof physiological state data as described above may be stored ingeneralized from in an expert knowledge database 204 and linked to,entered in, or associated with entries in a physiological sampledatabase 200. Documents may be stored and/or retrieved by classificationdevice 104 and/or language processing module 124 in and/or from adocument database 208; document database 208 may include any datastructure and/or data store suitable for use as physiological sampledatabase 200 as described above. Documents in document database 208 maybe linked to and/or retrieved using document identifiers such as URIand/or URL data, citation data, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which documents may be indexed and retrieved according tocitation, subject matter, author, date, or the like as consistent withthis disclosure.

Referring now to FIG. 4, an exemplary embodiment of an expert knowledgedatabase 204 is illustrated. Expert knowledge database 204 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 204 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 200 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 4, one or more database tables in expertknowledge database 204 may include, as a non-limiting example, an expertprognostic table 400. Expert prognostic table 400 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface120 as described above, one or more rows recording such an entry may beinserted in expert prognostic table 400. In an embodiment, a formsprocessing module 404 may sort data entered in a submission via firstgraphical user interface 120 by, for instance, sorting data from entriesin the first graphical user interface 120 to related categories of data;for instance, data entered in an entry relating in the first graphicaluser interface 120 to a prognostic label may be sorted into variablesand/or data structures for storage of prognostic labels, while dataentered in an entry relating to a category of physiological data and/oran element thereof may be sorted into variables and/or data structuresfor the storage of, respectively, categories of physiological data orelements of physiological data. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, language processingmodule 124 may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map physiological data to an existing label.Alternatively or additionally, when a language processing algorithm,such as vector similarity comparison, indicates that an entry is not asynonym of an existing label, language processing module 124 mayindicate that entry should be treated as relating to a new label; thismay be determined by, e.g., comparison to a threshold number of cosinesimilarity and/or other geometric measures of vector similarity of theentered text to a nearest existent label, and determination that adegree of similarity falls below the threshold number and/or a degree ofdissimilarity falls above the threshold number. Data from expert textualsubmissions 408, such as accomplished by filling out a paper or PDF formand/or submitting narrative information, may likewise be processed usinglanguage processing module 124. Data may be extracted from expert papers412, which may include without limitation publications in medical and/orscientific journals, by language processing module 124 via any suitableprocess as described herein. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalmethods whereby novel terms may be separated from already-classifiedterms and/or synonyms therefore, as consistent with this disclosure.Expert prognostic table 400 may include a single table and/or aplurality of tables; plurality of tables may include tables forparticular categories of prognostic labels such as a current diagnosistable, a future prognosis table, a genetic tendency table, a metabolictendency table, and/or an endocrinal tendency table (not shown), to namea few non-limiting examples presented for illustrative purposes only.

With continued reference to FIG. 4, one or more database tables inexpert knowledge database 204 may include, as a non-limiting example, anexpert causal table 416. Expert causal table 416 may contain one or morerecords indicating causal relationships, as described above, betweenprognostic labels, as provided by expert input. For instance, where atextual submission, expert paper, and/or entry via graphical userinterface 120 describes a condition associated with a first prognosticlabel as caused in part or fully by a second prognostic label, thatinformation may be recorded in expert causal table 416. One or moredatabase tables in expert knowledge database 204 may include, as anon-limiting example, an expert fundamental listing 424, which maycontain entries that indicate one or more prognostic labels that atleast an expert has identified as a root or fundamental cause of acondition associated with such labels and/or prognostic labelsidentified as caused by such labels.

Referring again to FIG. 2, a prognostic label database 212, which may beimplemented in any manner suitable for implementation of physiologicalsample database 200, may be used to store prognostic labels used insystem 100, including any prognostic labels correlated with elements ofphysiological data in first training set 108 as described above;prognostic labels may be linked to or refer to entries in physiologicalsample database 200 to which prognostic labels correspond. Linking maybe performed by reference to historical data concerning physiologicalsamples, such as diagnoses, prognoses, and/or other medical conclusionsderived from physiological samples in the past; alternatively oradditionally, a relationship between a prognostic label and a data entryin physiological sample database 200 may be determined by reference to arecord in an expert knowledge database 204 linking a given prognosticlabel to a given category of physiological sample as described above.Entries in prognostic label database 212 may be associated with one ormore categories of prognostic labels as described above, for instanceusing data stored in and/or extracted from an expert knowledge database204.

Referring now to FIG. 5, an exemplary embodiment of a prognostic labeldatabase 212 is illustrated. Prognostic label database 212 may, as anon-limiting example, organize data stored in the prognostic labeldatabase 212 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofprognostic label database 212 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 5, one or more database tables in prognosticlabel database 212 may include, as a non-limiting example, a sample datatable 500. Sample data table 500 may be a table listing sample data,along with, for instance, one or more linking columns to link such datato other information stored in prognostic label database 212. In anembodiment, sample data 504 may be acquired, for instance fromphysiological sample database 200, in a raw or unsorted form, and may betranslated into standard forms, such as standard units of measurement,labels associated with particular physiological data values, or thelike; this may be accomplished using a data standardization module 508,which may perform unit conversions or the like. Data standardizationmodule 508 may alternatively or additionally map textual information,such as labels describing values tested for or the like, using languageprocessing module 124 or equivalent components and/or algorithmsthereto.

Continuing to refer to FIG. 5, prognostic label database 212 may includea sample label table 512; sample label table 512 may list prognosticlabels received with and/or extracted from physiological samples, forinstance as received in the form of sample text 516. A languageprocessing module 124 may compare textual information so received toprognostic labels and/or form new prognostic labels according to anysuitable process as described above. A sample prognostic link table 520may combine samples with prognostic labels, as acquired from samplelabel table 512 and/or expert knowledge database 204; combination may beperformed by listing together in rows or by relating indices or commoncolumns of two or more tables to each other. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in expertknowledge database 204 consistently with this disclosure.

Still referring to FIG. 5, prognostic label database 212 may include acausal link table 524; causal link table 524 may identify, for a givenprognostic label, one or more prognostic labels identifying potentialcauses of the given prognostic label. Causal link table 524 may bepopulated, as a non-limiting example, from expert knowledge database424; for instance, and without limitation, classification device 104 maypopulate causal link table 524 using records from expert causal table416.

Referring again to FIG. 2, first training set 108 may be populated byretrieval of one or more records from physiological sample database 200and/or prognostic label database 212; in an embodiment, entriesretrieved from physiological sample database 200 and/or prognostic labeldatabase 212 may be filtered and or select via query to match one ormore additional elements of information as described above, so as toretrieve a first training set 108 including data belonging to a givencohort, demographic population, or other set, so as to generate outputsas described below that are tailored to a person or persons with regardto whom system 100 classifies physiological samples to prognostic labelsas set forth in further detail below. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which records may be retrieved from physiological sample database 200and/or prognostic label database to generate a first training set 108 toreflect individualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a physiological sample is being evaluatedas described in further detail below. Classification device 104 mayalternatively or additionally receive a first training set 108 and storeone or more entries in physiological sample database 200 and/orprognostic label database 212 as extracted from elements of firsttraining set 108.

Still referring to FIG. 2, second training set 128 may be populated byretrieval of one or more records from physiological sample database 200and/or prognostic label database 212; in an embodiment, entriesretrieved from physiological sample database 200 and/or prognostic labeldatabase 212 may be filtered and or select via query to match one ormore additional elements of information as described above, so as toretrieve a second training set 128 including data belonging to a givencohort, demographic population, or other set, so as to generate outputsas described below that are tailored to a person or persons with regardto whom system 100 classifies physiological samples to prognostic labelsas set forth in further detail below. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which records may be retrieved from physiological sample database 200and/or prognostic label database to generate a second training set 128to reflect individualized group data pertaining to a person of interestin operation of system and/or method, including without limitation aperson with regard to whom at least a physiological sample is beingevaluated as described in further detail below. Classification device104 may alternatively or additionally receive a second training set 128and store one or more entries in physiological sample database 200and/or prognostic label database 212 as extracted from elements ofsecond training set 128.

In an embodiment, and still referring to FIG. 2, classification device104 may receive an update to one or more elements of data represented infirst training set 108 and/or second training set 128, and may performone or more modifications to first training set 108 and/or secondtraining set 128, or to physiological sample database 200, expertknowledge database 204, and/or prognostic label database 212 as aresult. For instance a physiological sample may turn out to have beenerroneously recorded; classification device 104 may remove it from firsttraining set 108, second training set 128, physiological sample database200, expert knowledge database 204, and/or prognostic label database 212as a result. As a further example, a medical and/or academic paper, or astudy on which it was based, may be revoked; classification device 104may remove it from first training set 108, second training set 128,physiological sample database 200, expert knowledge database 204, and/orprognostic label database 212 as a result. Information provided by anexpert may likewise be removed if the expert loses credentials or isrevealed to have acted fraudulently.

Continuing to refer to FIG. 2, elements of data of first training set108, second training set 128, physiological sample database 200, expertknowledge database 204, and/or prognostic label database 212 may havetemporal attributes, such as timestamps; classification device 104 mayorder such elements according to recency, select only elements morerecently entered for first training set 108 and/or second training set128, or otherwise bias training sets, database entries, and/ormachine-learning models as described in further detail below toward morerecent or less recent entries. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which temporal attributes of data entries may be used to affectresults of methods and/or systems as described herein.

Referring again to FIG. 1, classification device 104 may be configuredto record at least a first biological extraction. At least a firstbiological extraction may include any element and/or elements of datasuitable for use as at least an element of physiological state data asdescribed above. At least a biological extraction may include aphysically extracted sample, where a “physically extracted sample” asused in this disclosure is a sample obtained by removing and analyzingtissue and/or fluid. Physically extracted sample may include withoutlimitation a blood sample, a tissue sample, a buccal swab, a mucoussample, a stool sample, a hair sample, a fingernail sample, or the like.Physically extracted sample may include, as a non-limiting example, atleast a blood sample. As a further non-limiting example, at least abiological extraction may include at least a genetic sample. At least agenetic sample may include a complete genome of a person or any portionthereof. At least a genetic sample may include a DNA sample and/or anRNA sample. At least a biological extraction may include an epigeneticsample, a proteomic sample, a tissue sample, a biopsy, and/or any otherphysically extracted sample. At least a biological extraction mayinclude an endocrinal sample. As a further non-limiting example, the atleast a biological extraction may include a signal from at least asensor configured to detect physiological data of a user and recordingthe at least a biological extraction as a function of the signal. Atleast a sensor may include any medical sensor and/or medical deviceconfigured to capture sensor data concerning a patient, including anyscanning, radiological and/or imaging device such as without limitationx-ray equipment, computer assisted tomography (CAT) scan equipment,positron emission tomography (PET) scan equipment, any form of magneticresonance imagery (MRI) equipment, ultrasound equipment, opticalscanning equipment such as photo-plethysmographic equipment, or thelike. At least a sensor may include any electromagnetic sensor,including without limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. At least a sensor may include atemperature sensor. At least a sensor may include any sensor that may beincluded in a mobile device and/or wearable device, including withoutlimitation a motion sensor such as an inertial measurement unit (IMU),one or more accelerometers, one or more gyroscopes, one or moremagnetometers, or the like. At least a wearable and/or mobile devicesensor may capture step, gait, and/or other mobility data, as well asdata describing activity levels and/or physical fitness. At least awearable and/or mobile device sensor may detect heart rate or the like.At least a sensor may detect any hematological parameter including bloodoxygen level, pulse rate, heart rate, pulse rhythm, and/or bloodpressure. At least a sensor may be a part of system 100 or may be aseparate device in communication with system 100.

Still referring to FIG. 1, at least a first biological extraction mayinclude data describing one or more test results, including results ofmobility tests, stress tests, dexterity tests, endocrinal tests, genetictests, and/or electromyographic tests, biopsies, radiological tests,genetic tests, and/or sensory tests. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of at least a physiological sample consistent withthis disclosure. At least a physiological sample may be added tophysiological sample database 200.

Continuing to refer to FIG. 1, system 100 includes a prognostic labellearner 144 operating on classification device 104, the prognostic labellearner 144 designed and configured to generate at least a firstprognostic output as a function of the first training set 108 and the atleast a biological extraction. Prognostic label learner 144 may includeany hardware and/or software module. Prognostic label learner 144 isdesigned and configured to generate outputs using machine learningprocesses. A machine learning process is a process that automatedly usesa body of data known as “training data” and/or a “training set” togenerate an algorithm that will be performed by a computingdevice/module to produce outputs given data provided as inputs; this isin contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 1, prognostic label learner 144 may be designedand configured to generate at least a prognostic output by creating atleast a first machine-learning model 148 relating physiological statedata to prognostic labels using the first training set 108 andgenerating the at least a prognostic output using the firstmachine-learning model 148; at least a first machine-learning model 148may include one or more models that determine a mathematicalrelationship between physiological state data and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.Machine-learning may include other regression algorithms, includingwithout limitation polynomial regression.

Continuing to refer to FIG. 1, machine-learning algorithm used togenerate first machine-learning model 148 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighborsalgorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naive Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1, prognostic label learner 144 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using first trainingset 108; the trained network may then be used to apply detectedrelationships between elements of physiological state data andprognostic labels.

Referring now to FIG. 6, machine-learning algorithms used by prognosticlabel learner 144 may include supervised machine-learning algorithms,which may, as a non-limiting example be executed using a supervisedlearning module 600 executing on classification device 104 and/or onanother computing device in communication with classification device104, which may include any hardware or software module. Supervisedmachine learning algorithms, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayuse elements of physiological data as inputs, prognostic labels asoutputs, and a scoring function representing a desired form ofrelationship to be detected between elements of physiological data andprognostic labels; scoring function may, for instance, seek to maximizethe probability that a given element of physiological state data and/orcombination of elements of physiological data is associated with a givenprognostic label and/or combination of prognostic labels to minimize theprobability that a given element of physiological state data and/orcombination of elements of physiological state data is not associatedwith a given prognostic label and/or combination of prognostic labels.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in first training set 108. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various possible variations of supervised machine learningalgorithms that may be used to determine relation between elements ofphysiological data and prognostic labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofparameters that have been suspected to be related to a given set ofprognostic labels, and/or are specified as linked to a medical specialtyand/or field of medicine covering a particular set of prognostic labels.As a non-limiting example, a particular set of blood test biomarkersand/or sensor data may be typically used by cardiologists to diagnose orpredict various cardiovascular conditions, and a supervisedmachine-learning process may be performed to relate those blood testbiomarkers and/or sensor data to the various cardiovascular conditions;in an embodiment, domain restrictions of supervised machine-learningprocedures may improve accuracy of resulting models by ignoringartifacts in training data. Domain restrictions may be suggested byexperts and/or deduced from known purposes for particular evaluationsand/or known tests used to evaluate prognostic labels. Additionalsupervised learning processes may be performed without domainrestrictions to detect, for instance, previously unknown and/orunsuspected relationships between physiological data and prognosticlabels.

Still referring to FIG. 6, machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 604 executing onclassification device 104 and/or on another computing device incommunication with classification device 104, which may include anyhardware or software module. An unsupervised machine-learning process,as used herein, is a process that derives inferences in datasets withoutregard to labels; as a result, an unsupervised machine-learning processmay be free to discover any structure, relationship, and/or correlationprovided in the data. For instance, and without limitation, prognosticlabel learner 144 and/or classification device 104 may perform anunsupervised machine learning process on first training set 108, whichmay cluster data of first training set 108 according to detectedrelationships between elements of the first training set 108, includingwithout limitation correlations of elements of physiological state datato each other and correlations of prognostic labels to each other; suchrelations may then be combined with supervised machine learning resultsto add new criteria for prognostic label learner 144 to apply inrelating physiological state data to prognostic labels. As anon-limiting, illustrative example, an unsupervised process maydetermine that a first element of physiological state data 112 acquiredin a blood test correlates closely with a second element ofphysiological state data, where the first element has been linked viasupervised learning processes to a given prognostic label, but thesecond has not; for instance, the second element may not have beendefined as an input for the supervised learning process, or may pertainto a domain outside of a domain limitation for the supervised learningprocess. Continuing the example a close correlation between firstelement of physiological state data and second element of physiologicalstate data may indicate that the second element is also a good predictorfor the prognostic label; second element may be included in a newsupervised process to derive a relationship or may be used as a synonymor proxy for the first physiological element by prognostic label learner144.

Still referring to FIG. 6, classification device 104 and/or prognosticlabel learner 144 may detect further significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or categories of prognostic labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added to system100, prognostic label learner 144 and/or classification device 104 maycontinuously or iteratively perform unsupervised machine-learningprocesses to detect relationships between different elements of theadded and/or overall data; in an embodiment, this may enable system 100to use detected relationships to discover new correlations between knownbiomarkers, prognostic labels, and/or ameliorative labels and one ormore elements of data in large bodies of data, such as genomic,proteomic, and/or microbiome-related data, enabling future supervisedlearning and/or lazy learning processes as described in further detailbelow to identify relationships between, e.g., particular clusters ofgenetic alleles and particular prognostic labels and/or suitableameliorative labels. Use of unsupervised learning may greatly enhancethe accuracy and detail with which system may detect prognostic labelsand/or ameliorative labels.

With continued reference to FIG. 6, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 6, prognostic label learner 144 mayalternatively or additionally be designed and configured to generate atleast a prognostic output by executing a lazy learning process as afunction of the first training set 108 and the at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule 608 executing on classification device 104 and/or on anothercomputing device in communication with classification device 104, whichmay include any hardware or software module. A lazy-learning processand/or protocol, which may alternatively be referred to as a “lazyloading” or “call-when-needed” process and/or protocol, may be a processwhereby machine learning is conducted upon receipt of an input to beconverted to an output, by combining the input and training set toderive the algorithm to be used to produce the output on demand. Forinstance, an initial set of simulations may be performed to cover a“first guess” at a prognostic label associated with biologicalextraction, using first training set 108. As a non-limiting example, aninitial heuristic may include a ranking of prognostic labels accordingto relation to a test type of at least a biological extraction, one ormore categories of physiological data identified in test type of atleast a biological extraction, and/or one or more values detected in atleast a biological extraction; ranking may include, without limitation,ranking according to significance scores of associations betweenelements of physiological data and prognostic labels, for instance ascalculated as described above. Heuristic may include selecting somenumber of highest-ranking associations and/or prognostic labels.Prognostic label learner 144 may alternatively or additionally implementany suitable “lazy learning” algorithm, including without limitation aK-nearest neighbors algorithm, a lazy naive Bayes algorithm, or thelike; persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various lazy-learning algorithms that maybe applied to generate prognostic outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

In an embodiment, and continuing to refer to FIG. 6, prognostic labellearner 144 may generate a plurality of prognostic labels havingdifferent implications for a particular person. For instance, where theat least a physiological sample includes a result of a dexterity test, alow score may be consistent with amyotrophic lateral sclerosis,Parkinson's disease, multiple sclerosis, and/or any number of less severdisorders or tendencies associated with lower levels of dexterity. Insuch a situation, prognostic label learner 144 and/or classificationdevice 104 may perform additional processes to resolve ambiguity.Processes may include presenting multiple possible results to a medicalpractitioner, informing the medical practitioner that one or morefollow-up tests and/or physiological samples are needed to furtherdetermine a more definite prognostic label. Alternatively oradditionally, processes may include additional machine learning steps;for instance, where reference to a model generated using supervisedlearning on a limited domain has produced multiple mutually exclusiveresults and/or multiple results that are unlikely all to be correct, ormultiple different supervised machine learning models in differentdomains may have identified mutually exclusive results and/or multipleresults that are unlikely all to be correct. In such a situation,prognostic label learner 144 and/or classification device 104 mayoperate a further algorithm to determine which of the multiple outputsis most likely to be correct; algorithm may include use of an additionalsupervised and/or unsupervised model. Alternatively or additionally,prognostic label learner 144 may perform one or more lazy learningprocesses using a more comprehensive set of user data to identify a moreprobably correct result of the multiple results. Results may bepresented and/or retained with rankings, for instance to advise amedical professional of the relative probabilities of various prognosticlabels being correct; alternatively or additionally, prognostic labelsassociated with a probability of correctness below a given thresholdand/or prognostic labels contradicting results of the additionalprocess, may be eliminated. As a non-limiting example, an endocrinaltest may determine that a given person has high levels of dopamine,indicating that a poor pegboard performance is almost certainly notbeing caused by Parkinson's disease, which may lead to Parkinson's beingeliminated from a list of prognostic labels associated with poorpegboard performance, for that person. Similarly, a genetic test mayeliminate Huntington's disease, or another disease definitively linkedto a given genetic profile, as a cause. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which additional processing may be used to determine relativelikelihoods of prognostic labels on a list of multiple prognosticlabels, and/or to eliminate some labels from such a list. Prognosticoutput 612 may be provided to a user output device as described infurther detail below.

Referring again to FIG. 1, classification device 104 includes a causallink learner 152 operating on the classification device 104, the causallink learner 152 designed and configured to generate the at least asecond prognostic output 712 as a function of the second training set128 and the at least a first prognostic output. Causal link learner 152may generate a second machine-learning model 156 to generate the atleast a second prognostic output 712, where second machine-learningmodel 156 may be generated using any process or combination of processessuitable for generation of first machine-learning model 148 as describedabove. At least a second prognostic output 712 may be causally linked toat least a first prognostic output. As used herein, at least a secondprognostic output 712 is “causally linked” to at least a firstprognostic output if a prognostic label in at least a second prognosticoutput 712 identifies a potential cause of a prognostic label in atleast a first prognostic output, and/or a prognostic label in at least afirst prognostic output identifies a potential cause of a prognosticlabel in at least a second prognostic output 712. A “potential cause,”as used in the above definition of “causally linked” indicates a firstphenomenon, such as physiological condition or other condition that maybe labeled using a prognostic label, that has been identified as causinga second phenomenon, such as a physiological condition or othercondition that may be labeled using a prognostic label, in at least onecase; the first phenomenon, and/or the prognostic label associatedtherewith, is described for purposes herein as a “potential cause” forthe second phenomenon. For instance, a genetic condition or mutationthat causes elevated cholesterol in the blood of persons possessing thatgenetic condition or mutation is a “potential cause” ofhypercholesterolemia, as it has been identified in some cases as causingthe latter condition, while not necessarily being a cause in all cases.In an embodiment, at least a second prognostic output 712 may representa cause of the at least a first prognostic output, which indicates, asused herein, that a prognostic label in at least a second prognosticoutput 712 identifies a potential cause of a prognostic label in atleast a first prognostic output.

Referring now to FIG. 7, machine-learning algorithms used by causal linklearner 152 may include supervised machine-learning algorithms, whichmay, as a non-limiting example be executed using a supervised learningmodule 700 executing on classification device 104 and/or on anothercomputing device in communication with classification device 104, whichmay include any hardware or software module; supervised learning may beperformed as described above in reference to FIG. 6. For instance, asupervised learning algorithm may use at least a first prognosticoutput, prognostic labels from at least a first prognostic output,and/or elements of physiological data as inputs, prognostic labels asoutputs, and a scoring function representing a desired form ofrelationship to be detected between at least a first prognostic output,one or more prognostic labels from at least a first prognostic output,and/or elements of physiological data and prognostic labels; scoringfunction may, for instance, seek to maximize the probability that agiven first prognostic label 116 and/or element of physiological statedata and/or combination of elements of physiological data is associatedwith a given second prognostic label and/or combination of prognosticlabels to minimize the probability that a given first prognostic label116 and/or element of physiological state data and/or combination ofelements of physiological state data is not associated with a givenprognostic label and/or combination of prognostic labels. Scoringfunction may be expressed as a risk function representing an “expectedloss” of an algorithm relating inputs to outputs, where loss is computedas an error function representing a degree to which a predictiongenerated by the relation is incorrect when compared to a giveninput-output pair provided in first training set 108. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various possible variations of supervised machine learning algorithmsthat may be used to determine relation between elements of physiologicaldata and prognostic labels. In an embodiment, one or more supervisedmachine-learning algorithms may be restricted to a particular domain forinstance, a supervised machine-learning process may be performed withrespect to a given set of parameters and/or categories of parametersthat have been suspected to be related to a given set of prognosticlabels, and/or are specified as linked to a medical specialty and/orfield of medicine covering a particular set of prognostic labels. As anon-limiting example, a particular set of blood test biomarkers and/orsensor data may be typically used by cardiologists to diagnose orpredict various cardiovascular conditions, and a supervisedmachine-learning process may be performed to relate those blood testbiomarkers and/or sensor data to the various cardiovascular conditions;in an embodiment, domain restrictions of supervised machine-learningprocedures may improve accuracy of resulting models by ignoringartifacts in training data. Domain restrictions may be suggested byexperts and/or deduced from known purposes for particular evaluationsand/or known tests used to evaluate prognostic labels. Additionalsupervised learning processes may be performed without domainrestrictions to detect, for instance, previously unknown and/orunsuspected relationships between physiological data and prognosticlabels.

Still referring to FIG. 7, machine-learning algorithms may includeunsupervised processes; unsupervised processes may, as a non-limitingexample, be executed by an unsupervised learning module 704 executing onclassification device 104 and/or on another computing device incommunication with classification device 104, which may include anyhardware or software module. An unsupervised machine-learning process,as used herein, is a process that derives inferences in datasets withoutregard to labels; as a result, an unsupervised machine-learning processmay be free to discover any structure, relationship, and/or correlationprovided in the data. For instance, and without limitation, causal linklearner 152 and/or classification device 104 may perform an unsupervisedmachine learning process on first training set 108, which may clusterdata of first training set 108 according to detected relationshipsbetween elements of the first training set 108, including withoutlimitation correlations of elements of physiological state data to eachother and/or correlations of prognostic labels to each other; suchrelations may then be combined with supervised machine learning resultsto add new criteria for causal link learner 152 to apply in relatingphysiological state data to prognostic labels.

Still referring to FIG. 7, classification device 104 and/or causal linklearner 152 may detect further significant categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above; such newly identified categories, as well as categoriesentered by experts in free-form fields as described above, may be addedto pre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to system 100, causal linklearner 152 and/or classification device 104 may continuously oriteratively perform unsupervised machine-learning processes to detectrelationships between different elements of the added and/or overalldata; in an embodiment, this may enable system 100 to use detectedrelationships to discover new correlations between known biomarkers,prognostic labels, and/or ameliorative labels and one or more elementsof data in large bodies of data, such as genomic, proteomic, and/ormicrobiome-related data, enabling future supervised learning and/or lazylearning processes as described in further detail below to identifyrelationships between, e.g., particular clusters of genetic alleles andparticular prognostic labels. Use of unsupervised learning may greatlyenhance the accuracy and detail with which system may detect prognosticlabels and/or ameliorative labels.

With continued reference to FIG. 7, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 7, causal link learner 152 may alternatively oradditionally be designed and configured to generate at least a secondprognostic output 712 by executing a lazy learning process as a functionof the first training set 108 and the at least a biological extraction;lazy learning processes may be performed by a lazy learning module 708executing on classification device 104 and/or on another computingdevice in communication with classification device 104, which mayinclude any hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata second prognostic label associated with and or potentially causing afirst prognostic label 116, using second training set 128. As anon-limiting example, an initial heuristic may include a ranking ofprognostic labels according to a given prognostic label in firstprognostic output, one or more categories associated with a givenprognostic label in first prognostic output, or the like; ranking mayinclude, without limitation, ranking according to significance scores ofassociations between prognostic labels, for instance as calculated asdescribed above. Heuristic may include selecting some number ofhighest-ranking associations and/or prognostic labels. Causal linklearner 152 may alternatively or additionally implement any suitable“lazy learning” algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naive Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate prognostic outputs as described in this disclosure, includingwithout limitation lazy learning applications of machine-learningalgorithms as described in further detail below. At least a secondprognostic output 712 712 may be provided to a user output device asdescribed in further detail below.

Referring again to FIG. 1, classification device 104 may be configuredto transmit an output including at least a first prognostic output andat least a second prognostic output 712 712 to a user output device 160.A user output device 160 may include, without limitation, a display incommunication with classification device 104; display may include anydisplay as described in this disclosure. A user output device 160 mayinclude an addition computing device, such as a mobile device, laptop,desktop computer, or the like; as a non-limiting example, the useroutput device 160 may be a computer and/or workstation operated by amedical professional. Output may be displayed on at least a user outputdevice 160 using an output graphical user interface 120; outputgraphical user interface 120 may display one or more prognostic labelsof at least a first prognostic output and/or at least a secondprognostic output 712. Alternatively or additionally, prognostic labelsmay be translated into display data including without limitation textualdescriptions corresponding to prognostic labels, one or more imagesassociated with prognostic labels, and/or one or more video or audiofiles associated with prognostic labels; each of the above-describeddisplay data may be retrieved from a display data store, which may, forinstance associate or link prognostic labels and/or elements ofphysiological data with one or more display data. Where output includesmultiple prognostic labels, classification device 104 may cause to auser output device 160 to display the multiple labels and/or displaydata associated therewith; labels may be displayed according to rankingsas described above, including without limitation rankings of prognosticlabels according to probability of correctness or the like. Significancescores, as calculated above, may be used to filter outputs as describedin further detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.

With continued reference to FIG. 1, classification device 104 maydisplay one or more elements of contextual information, includingwithout limitation any patient medical history such as current labresults, a current reason for visiting a medical professional, currentstatus of one or more currently implemented treatment plans,biographical information concerning the patient, and the like. One ormore elements of contextual information may include goals a patientwishes to achieve with a medical visit or session, and/or as result ofinteraction with system 100. Contextual information may include one ormore questions a patient wishes to have answered in a medical visitand/or session, and/or as a result of interaction with system 100.Contextual information may include one or more questions to ask apatient. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various forms of contextual informationthat may be included, consistently with this disclosure. System 100 mayrecord a conversation between a patient and a medical professional forlater entry into medical records.

With continued reference to FIG. 1, classification device 104 may beconfigured to display one or more follow-up suggestions at a user outputdevice 160. One of more follow-up suggestions may include, withoutlimitation, suggestions for acquisition of an additional biologicalextraction; in an embodiment, additional biological extraction may beprovided to classification device 104, which may trigger repetition ofone or more processes as described above, including without limitationgeneration of prognostic output, refinement or elimination of ambiguousprognostic labels of prognostic output, generation of ameliorativeoutput, and/or refinement or elimination of ambiguous ameliorativelabels of ameliorative output. For instance, where a pegboard testresult suggests possible diagnoses of Parkinson's disease, Huntington'sdisease, ALS, and MS as described above, follow-up suggestions mayinclude suggestions to perform endocrinal tests, genetic tests, and/orelectromyographic tests; results of such tests may eliminate one or moreof the possible diagnoses, such that a subsequently displayed outputonly lists conditions that have not been eliminated by the follow-uptest. Follow-up tests may include any receipt of any physiologicalsample as described above.

In an embodiment, and still referring to FIG. 1, causal link learner 152may output more than one second prognostic output 712; for instance, atleast a first prognostic output may have more than one associatedprognostic labels indicative of causes for conditions identified by atleast a first prognostic label 116. In some cases such a multiplicity ofpotential causes may indicate that multiple factors are contributing toa cause of at least a first prognostic output; system 100 and/orclassification device 104 may display multiple second prognostic outputs712 to a user such as a medical professional, or may repeat one or moremethod steps as described in this disclosure to iterate from multiplesecond prognostic outputs 712 to third or additional prognostic outputsassociated with fundamental or root causes, for instance by locating oneor more causally linked fundamental prognostic labels. Alternatively oradditionally, classification device 104 and/or system 100 may beconfigured to select a second prognostic output 712 from the pluralityof second prognostic outputs 712; system 100 and/or classificationdevice 104 may select one or more second prognostic outputs 712 fromplurality of prognostic outputs, where selection may indicate a highercurrent degree of importance for ameliorative or alleviative purposesand/or elimination of one or more potential causes that are incorrectchoices in this case. As a non-limiting example, a person complaining ofjoint pain may be linked to a first prognostic output associated withjoint inflammation, which may in turn be linked, via causal link learner152, to one second prognostic output 712 associated with gout andanother second prognostic output 712 associated with rheumatoidarthritis; the person may be suffering from the latter and not theformer, and system 100 may engage in one or more processes to select acorrect second prognostic label.

With continued reference to FIG. 1, processes may include additionalmachine learning steps; for instance, where reference to a modelgenerated using supervised learning on a limited domain has producedmultiple mutually exclusive results and/or multiple results that areunlikely all to be correct, or multiple different supervised machinelearning models in different domains may have identified mutuallyexclusive results and/or multiple results that are unlikely all to becorrect. In such a situation, causal link learner 152 and/orclassification device 104 may operate a further algorithm to determinewhich of the multiple outputs is most likely to be correct; algorithmmay include use of an additional supervised and/or unsupervised model.Alternatively or additionally, causal link learner 152 may perform oneor more lazy learning processes using a more comprehensive set of userdata to identify a more probably correct result of the multiple results.Results may be presented and/or retained with rankings, for instance toadvise a medical professional of the relative probabilities of variousprognostic labels being correct; alternatively or additionally,prognostic labels associated with a probability of correctness below agiven threshold and/or prognostic labels contradicting results of theadditional process, may be eliminated. As a non-limiting example, anendocrinal test may determine that a given person has high levels ofdopamine, indicating that a poor pegboard performance is almostcertainly not being caused by Parkinson's disease, which may lead toParkinson's being eliminated from a list of prognostic labels associatedwith poor pegboard performance, for that person. Similarly, a genetictest may eliminate Huntington's disease, or another disease definitivelylinked to a given genetic profile, as a cause. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious ways in which additional processing may be used to determinerelative likelihoods of prognostic labels on a list of multipleprognostic labels, and/or to eliminate some labels from such a list.

As a further non-limiting example, and still referring to FIG. 1,classification device 104 is further configured to receive at least asecond biological extraction and select the second prognostic output 712from the plurality of second prognostic outputs 712 as a function of theat least a second biological extraction; at least a second biologicalextraction may include any data suitable for use as at least a firstbiological extraction as described above. In an embodiment, at least asecond biological extraction may be used directly to filter plurality ofsecond prognostic outputs 712; for instance, a given prognostic label insecond prognostic output 712 may be listed in prognostic link table ofphysiological sample database as associated with a particular result ofa test or a particular value as determined by at least a secondbiological extraction, such that classification device 104 may selectthe prognostic label as matching, or reject the prognostic label as notmatching, the at least a second biological extraction. As anon-limiting, illustrative example, where at least a second prognosticoutput 712 include one prognostic output associated with gout as a causeof joint inflammation as identified in at least a first prognosticoutput and another prognostic output associated with rheumatoidarthritis as a cause of the joint inflammation, prognostic link tablemay indicate that the former is associated with elevated urea levels;classification device 104 may therefore eliminate gout from at least asecond prognostic output 712 where at least a second biologicalextraction indicates a low level of urea.

Alternatively or additionally, and still referring to FIG. 1, prognosticlabel learner 144 may be further configured to generate a thirdprognostic output as a function of the first training set 108 and the atleast a second biological extraction; this may be performed, withoutlimitation, as described above. Classification device 104 may be furtherconfigured to select a second prognostic output 712 from a plurality ofsecond prognostic outputs 712 as a function of the third prognosticoutput. Selection may include selecting a second prognostic output 712by determining that the second prognostic output 712 matches thirdprognostic output; for instance, and without limitation, causal linklearner 152 may generate an additional output matching a secondprognostic output 712, indicating that the second prognostic output 712is a likely correct answer, and/or third prognostic output may itselfmatch an output of second prognostic output 712. Selecting may includeselecting a second prognostic output 712 by determining that aprognostic output of a plurality of second prognostic outputs 712contradicts a third prognostic output. For instance, and withoutlimitation, third prognostic output may represent a mutually exclusivealternative to a second prognostic output 712, indicating that thesecond prognostic output 712 is unlikely to be correct, and/or may belinked by way of generating another output from causal link learner 152to a prognostic output matching a different prognostic label from thatin a second prognostic output 712, which may be eliminated ascontradictory. As a non-limiting example, an elevated urea level may bedetected in second biological extraction, mapping to a third prognosticoutput associated with gout; gout may be selected from a plurality ofsecond prognostic outputs 712, and/or rheumatoid arthritis eliminatedfrom plurality of second prognostic outputs 712, as a result.

With continued reference to FIG. 1, and as another non-limiting example,classification device 104 may be configured to receive a userinstruction selecting one of the plurality of second classificationdevice 104s and select the second prognostic output 712 as a function ofthe user instruction. For instance, and as indicated above, multiplepossible results associated with plurality of second prognostic outputs712 may be displayed, conveyed to a user output device 160, or the like;a medical professional may , of instance, select a second prognosticoutput 712 of a plurality of prognostic outputs using his or her medicaljudgement, combined with patient history, test results, or the like.Processes for selection may, as a further non-limiting example, includepresenting multiple possible results to a medical practitioner,informing the medical practitioner that one or more follow-up testsand/or biological extractions are needed to further determine a moredefinite prognostic label; such follow-up tests may be used to obtain atleast a second biological extraction to be used as described above.

Still referring to FIG. 1, system 100 and/or classification device 104may be configured to determine that the at least a second prognosticoutput 712 includes a fundamental prognostic label. A “fundamentalprognostic label,” as used herein, is a prognostic label having noadditional prognostic label identified as causing it. For instance, afundamental prognostic label may identify a gene combination, as definedin U.S. Nonprovisional application Ser. No. 16/590,426, filed on Oct. 2,2019 and entitled “SYSTEMS AND METHODS FOR GENERATING A GENOTYPIC CAUSALMODEL OF A DISEASE STATE,” the entirety of which is incorporated byreference herein. As a further non-limiting example, a fundamentalprognostic label may identify one or more chemicals, pathogens, and/ordietary elements that cause a prognostic label of at least a firstprognostic output upon introduction into a person's body. Determinationmay include identifying, in a listing and/or data structure offundamental prognostic labels, such as fundamental label listing 164,the at least a second prognostic output 712, and/or a second prognosticoutput 712 of the at least a second prognostic output 712. In anembodiment, classification device 104 may query a fundamental labellisting 164, which may list prognostic labels and indications of whetherprognostic labels are fundamental prognostic labels; fundamental labellisting 164 may, as a non-limiting example, contain only fundamentalprognostic labels. Fundamental label listing 164 may include an entry byan expert identifying a prognostic label of the at least a secondprognostic output as a fundamental prognostic label. Classificationdevice 104 may use expert fundamental listing 424 to populatefundamental label listing 164. For instance, and without limitation,classification device 104 may perform a statistical process, such aswithout limitation any process described above as usable formachine-learning and/or language processing, to identify fundamentalprognostic labels based on entries in expert fundamental listing;statistical processes may include, without limitation, enumeration ofentries in the expert fundamental listing 424 indicating that a givenprognostic label is fundamental and comparison of such enumerations tothreshold numbers. Alternatively or additionally, classification devicemay determine whether a given prognostic label is a fundamentalprognostic label by directly querying expert fundamental listing 424.Computing device may alternatively or additionally determine that the atleast a second prognostic output includes a fundamental prognostic labelby determining a number of entries in second training set identifying aprognostic label of the at least a second prognostic output as caused bya fourth prognostic label, and determining that the number of entries iszero and/or fails a threshold comparison, where a “threshold comparison”indicates having a threshold number, percentage, or other numericalelement stored in memory and comparing the number of entries to thatthreshold number, percentage, or other numerical element; computingdevice may be configured to identify a result of comparison as passingthe comparison, and another result as failing it, such as withoutlimitation a number of entries being less than a threshold number, apercentage of entries being less than a threshold percentage, or thelike.

Alternatively or additionally, and still referring to FIG. 1,classification device 104 may be configured to receive a user indicationthat at least a second prognostic output 712 and/or a second prognosticoutput 712 of the at least a second prognostic output 712 represents afundamental prognostic label. A medical professional may, as anon-limiting example, identify at least a second prognostic output 712as a fundamental cause of a condition identified in at least a firstprognostic output and enter a user instruction indicating the at least asecond prognostic output 712 is a fundamental cause of the firstprognostic output and/or the condition indicated thereby. A medicalprofessional may, as a non-limiting example, identify at least a secondprognostic output 712 as not being a fundamental cause of a conditionidentified in at least a first prognostic output and enter a userinstruction indicating the at least a second prognostic output 712 isnot a fundamental cause of the first prognostic output and/or thecondition indicated thereby, and/or may enter an instruction to repeatone or more steps described above, such as an instruction that causescausal link learner 152 to generate at least a third or subsequentprognostic output that indicates a cause of at least a second prognosticoutput 712; any step described in this disclosure may then be used todetermine whether the at least a third or subsequent prognostic outputidentifies and/or includes a fundamental prognostic label, withadditional repetitions of the above process being performed untildiscovery of a fundamental prognostic label and/or user instruction tocease iteration. Iterative use of causal link learner 152 to find atleast a prognostic output identifying a cause of a previously generateat least a prognostic output may be performed automatically; forinstance, where a given prognostic output is not identified asfundamental in a listing and/or database as described above,classification device 104 may cause causal link learner 152 torepeatedly output causally linked prognostic labels until a fundamentalprognostic label is generated, and/or until causal link learner 152cannot generate further causally linked prognostic labels and/oroutputs.

Embodiments of system 100 may furnish augmented intelligence systemsthat facilitate diagnostic, prognostic, curative, and/or therapeuticdecisions by medical professionals such as doctors. System 100 mayprovide fully automated tools and resources for each doctor to handle,process, diagnosis, develop treatment plans, facilitate and monitor allpatient implementation, and record each patient status. Provision ofexpert system elements via expert inputs and document-driven languageanalysis may ensure that recommendations generated by system 100 arebacked by the very best medical knowledge and practices in the world.Models and/or learners with access to data in depth may enablegeneration of recommendations that are directly personalized for eachpatient, providing complete confidence, mitigated risk, and completetransparency. Access to well-organized and personalized knowledge indepth may greatly enhance efficiency of medical visits; in embodiments,a comprehensive visit may be completed in as little as 10 minutes.Recommendations may further suggest follow up testing and/or therapy,ensuring an effective ongoing treatment and prognostic plan.

Turning now to FIG. 8, an exemplary embodiment of a method 800 ofcausative chaining of prognostic label classifications is illustrated.At step 805, a classification device 104 receives training data.Training data may include any training data as described above inreference to 1-7. Receiving training data may include receiving a firsttraining set 108 including a plurality of first data entries, each firstdata entry of the plurality of first data entries including at least afirst element of physiological state data and at least a correlatedfirst prognostic label 116; this may be implemented, without limitation,as described above in reference to FIGS. 1-7. Receiving training datamay include receiving a second training set 128 including a plurality ofsecond data entries, each second data entry of the plurality of seconddata entries including at least a second prognostic label 132 and atleast a correlated third prognostic label; this may be implemented asdescribed above in reference to FIGS. 1-7. Second training set 128 mayinclude at least a data entry including at least a second element ofphysiological state data 140 and at least a correlated fourth prognosticlabel, for instance and without limitation as described above inreference to FIGS. 1-7. At step 810, classification device 104 recordsat least a first biological extraction.

Still referring to FIG. 8, at step 815, classification device 104generates at least a first prognostic output as a function of the firsttraining set 108 and the at least a physiological test sample. This maybe implemented as described above in reference to FIGS. 1-7. At step820, classification device 104 generates at least a second prognosticoutput 712 as a function of the second training set 128 and the at leasta first prognostic output, where the at least a second prognostic output712 represents a cause of the at least a first prognostic output; thismay be implemented as described above in reference to FIGS. 1-7.Classification device 104 may generate second prognostic output 712 bygenerating the second prognostic output 712 as a function of the secondtraining set 128, the first prognostic output, and the at least abiological extraction. For instance, classification device 104 and/orcausal link learner 152 may generate a model relating a mathematicalfunction of physiological state data and prognostic labels to prognosticlabels, and/or may detect such a relationship using a lazy learningalgorithm as described above in reference to FIGS. 1-7. In such anembodiment, this may permit classification device 104 and/or causal linklearner 152 to select at least a second prognostic output 712 thatrepresents a potential cause of at least a first prognostic output andis consistent with at least a first biological extraction;classification device 104 and/or causal link learner 152 mayalternatively or additionally regenerate at least a second prognosticoutput 712 as a function of the first prognostic output, the secondtraining set 128, and at least a second biological extraction, and/or asa function of the first prognostic output, the second training set 128,the at least a second biological extraction and the at least a firstbiological extraction, to select a second biological extraction from aplurality of second biological extractions as described above.

Still referring to FIG. 8, in an embodiment, where at least a secondprognostic output 712 further comprises a plurality of second prognosticoutputs 712, classification device 104 may select a second prognosticoutput 712 from the plurality of second prognostic outputs 712; this maybe implemented according to any process or process step described above.For instance, and without limitation, selecting the second prognosticoutput 712 may include receiving at least a second biological extractionand selecting the second prognostic output 712 from the plurality ofsecond prognostic outputs 712 as a function of the at least a secondbiological extraction, which may be performed according to any processor process steps as described in this disclosure. Selecting secondprognostic output 712 from the plurality of prognostic outputs based onthe at least a second biological extraction may include generating athird prognostic output as a function of the first training set 108 andthe at least a second biological extraction and selecting secondprognostic output 712 from the plurality of second prognostic outputs712 as a function of the third prognostic output, for instance asdescribed above in reference to FIGS. 1-7. Selecting a second prognosticoutput 712 from the plurality of second prognostic outputs 712 mayinclude selecting the second prognostic output 712 by determining thatthe second prognostic output 712 matches third prognostic output, forinstance as described above in reference to FIGS. 1-7. Selecting asecond prognostic output 712 from the plurality of second prognosticoutputs 712 may include determining that a prognostic output of theplurality of second prognostic outputs 712 contradicts third prognosticoutput, for instance as described above in reference to FIGS. 1-7.Selecting the second prognostic output 712 from the plurality of secondprognostic outputs 712 may include receiving a user instructionselecting one of the plurality of second classification device 104s andselecting the second prognostic output 712 as a function of the userinstruction; this may be implemented as described above in reference toFIGS. 1-7.

In an embodiment, and still referring to FIG. 8, classification device104 may determine that the at least a second prognostic output 712includes a fundamental prognostic label. This determination may beperformed according to any process or process steps described above inreference to FIG. 1-7. Any step or steps of method 800 may be repeated,in any order. As a non-limiting example, steps of method 800 may beperformed iteratively to find one or more fundamental prognostic labelsassociated with at least a first prognostic label 116, for instance asdescribed elsewhere in this disclosure.

Turning now to FIG. 9, an exemplary embodiment of a method 900 ofcausative chaining of prognostic label classifications is illustrated.At step 905, at least a first prognostic output is provided at aclassification device 104. At least a first prognostic output may begenerated using any process described in this disclosure for generationof a prognostic output, including any step or steps of method 800. Atleast a first prognostic output may have been generated by a previousiteration of an embodiment of method 900; in other words, at least afirst prognostic output may include and/or be at least a secondprognostic output produced in a previous iteration of an embodiment ofmethod 900.

At step 910, and still referring to FIG. 9, classification device 104determines that first prognostic output does not include a fundamentalprognostic label; classification device 104 may make this determination,using any component, components, and/or steps described above inreference to FIGS. 1-8. For instance, and without limitation,classification device 104 may determine that first prognostic outputdoes not include a fundamental prognostic label by querying fundamentallabel listing 164 and/or expert fundamental listing 424; alternativelyor additionally, a user such as a medical professional may enter aninstruction indicating that the at least a first prognostic output doesnot include a fundamental prognostic label.

At step 915, and with continued reference to FIG. 9, classificationdevice receives a training set including a plurality of data entries,each data entry of the plurality of data entries including at least afirst prognostic label 116 and at least a correlated second prognosticlabel 132; this may be accomplished as described above, in reference toFIGS. 1-8, for reception of second training set 128.

At step 920, and still referring to FIG. 9, classification device 104generates at least a second prognostic output as a function of thetraining set and the at least a first prognostic output, wherein the atleast a second prognostic output represents a cause of the at least afirst prognostic output; this may be performed as described above inreference to FIGS. 1-8, for instance and without limitation as describedfor generation of at least a second prognostic output 712.

In an embodiment, and still referring to FIG. 9, one or more methodsteps in this disclosure, including without limitation one or more stepsof embodiments of method 900, may be repeated or performed iteratively.For instance, and without limitation, method 900 as described above maybe repeatedly performed, with each iteration using a prognostic outputof the at least a second prognostic output of the previous iteration asthe at least a first prognostic output for the current iteration, untila termination condition occurs. Termination condition may includeidentification of a second prognostic output as containing a fundamentalprognostic label, where identification may be performed according to anyprocess for determining that a prognostic label is a fundamentalprognostic label as described above. Termination condition may includeentry of a user command to terminate. In lieu of detection that firstprognostic output does not include a fundamental prognostic label, analternative embodiment of method 900 may include receiving a usercommand to generate second prognostic output; a user, such as a medicalprofessional, may reenter such commands until satisfied with the numberof iterations and/or the second prognostic output generated.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof In one example, a computing device may includeand/or be included in a kiosk.

FIG. 10 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1000 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1000 includes a processor 1004 and a memory1008 that communicate with each other, and with other components, via abus 1012. Bus 1012 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 1008 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1016 (BIOS), including basic routines thathelp to transfer information between elements within computer system1000, such as during start-up, may be stored in memory 1008. Memory 1008may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1020 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1008 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1000 may also include a storage device 1024. Examples ofa storage device (e.g., storage device 1024) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1024 may beconnected to bus 1012 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1024 (or one or more components thereof) may be removably interfacedwith computer system 1000 (e.g., via an external port connector (notshown)). Particularly, storage device 1024 and an associatedmachine-readable medium 1028 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1000. In one example,software 1020 may reside, completely or partially, withinmachine-readable medium 1028. In another example, software 1020 mayreside, completely or partially, within processor 1004.

Computer system 1000 may also include an input device 1032. In oneexample, a user of computer system 1000 may enter commands and/or otherinformation into computer system 1000 via input device 1032. Examples ofan input device 1032 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof Input device 1032may be interfaced to bus 1012 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1012, and any combinations thereof. Input device 1032may include a touch screen interface that may be a part of or separatefrom display 1036, discussed further below. Input device 1032 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1000 via storage device 1024 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1040. A networkinterface device, such as network interface device 1040, may be utilizedfor connecting computer system 1000 to one or more of a variety ofnetworks, such as network 1044, and one or more remote devices 1048connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1044, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1020, etc.) may be communicated to and/or fromcomputer system 1000 via network interface device 1040.

Computer system 1000 may further include a video display adapter 1052for communicating a displayable image to a display device, such asdisplay device 1036. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1052 and display device 1036 maybe utilized in combination with processor 1004 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1000 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1012 via a peripheral interface 1056.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for causative chaining of prognosticlabel classifications, the system comprising: at least a computingdevice, the computing device designed and configured to: receivetraining data, wherein receiving the training data further comprises:receiving a first training set including a plurality of first dataentries, each first data entry of the plurality of first data entriesincluding at least a first element of physiological state data and atleast a correlated first prognostic label; and receiving a secondtraining set including a plurality of second data entries, each seconddata entry of the plurality of second data entries including at least asecond prognostic label and at least a correlated third prognosticlabel; record at least a first biological extraction; a prognostic labellearner operating on the at least a computing device, the prognosticlabel learner designed and configured to generate at least a firstprognostic output as a function of the first training set and the atleast a physiological test sample, wherein the prognostic label learneris further configured to generate a third prognostic output as afunction of the first training set and the at least a second biologicalextraction; and a causal link learner operating on the at least acomputing device, the causal link learner designed and configured togenerate at least a second prognostic output as a function of the secondtraining set and the at least a first prognostic output, wherein the atleast a second prognostic output represents a cause of the at least afirst prognostic output, and wherein the at least a second prognosticoutput further comprises a plurality of second prognostic outputs;wherein the at least a computing device is further configured todetermine that the at least a second prognostic output includes afundamental prognostic label.
 2. The system of claim 1, wherein thesecond training set further comprises at least a data entry including atleast a second element of physiological data and at least a correlatedfourth prognostic label.
 3. The system of claim 2, wherein the causallink learner is further configured to generate the second prognosticoutput as a function of the second training set, the first prognosticoutput, and the at least a biological extraction.
 4. The system of claim1, wherein the at least a computing device is configured to: generate athird prognostic output; and select a second prognostic output from theplurality of second prognostic outputs by determining that the secondprognostic output matches the third prognostic output.
 5. The system ofclaim 1, wherein the computing device is further configured to generatethe at least a second prognostic output by executing a K-nearestneighbors algorithm as a function of the second training set and the atleast a first prognostic output.
 6. The system of claim 1, wherein thecomputing device is further configured to determine that the at least asecond prognostic output includes a fundamental prognostic label byidentifying a prognostic label of the at least a second prognosticoutput in a fundamental label listing.
 7. The system of claim 6, whereinthe fundamental label listing includes an entry by an expert identifyinga prognostic label of the at least a second prognostic output as afundamental prognostic label.
 8. The system of claim 1, wherein thecomputing device is further configured to determine that the at least asecond prognostic output includes a fundamental prognostic label by:determining a number of entries in the second training set identifying aprognostic label of the at least a second prognostic output as caused bya fourth prognostic label; and determining that the number of entriesfails a threshold comparison.
 9. The system of claim 1, wherein thecomputing device is further configured to: display one or more follow-upsuggestions for acquisition of at least a second biological extractionat a user output device; and receive the at least a second biologicalextraction;
 10. The system of claim 1 wherein the at least a computingdevice is further configured to: receive at least a second biologicalextraction; generate a third prognostic output as a function of thefirst training set and the at least a second biological extraction;determine that a single prognostic output of the plurality of secondprognostic outputs contradicts the third prognostic output; andeliminate the single prognostic output from the plurality of secondprognostic outputs.
 11. A method of causative chaining of prognosticlabel classifications, the method comprising: receiving, by at least acomputing device, training data, wherein receiving the training datafurther comprises: receiving a first training set including a pluralityof first data entries, each first data entry of the plurality of firstdata entries including at least a first element of physiological statedata and at least a correlated first prognostic label; and receiving asecond training set including a plurality of second data entries, eachsecond data entry of the plurality of second data entries including atleast a second prognostic label and at least a correlated thirdprognostic label; and recording, by the at least a computing device, atleast a first biological extraction; generating, by the computingdevice, at least a first prognostic output as a function of the firsttraining set and the at least a physiological test sample; generating,by the at least a computing device, at least a second prognostic outputas a function of the second training set and the at least a firstprognostic output, wherein the at least a second prognostic outputrepresents a cause of the at least a first prognostic output; anddetermining that the at least a second prognostic output includes afundamental prognostic label.
 12. The method of claim 11, wherein thesecond training set further comprises at least a data entry including atleast a second element of physiological data and at least a correlatedfourth prognostic label.
 13. The method of claim 12, wherein generatingthe second prognostic output further comprises generating the secondprognostic output as a function of the second training set, the firstprognostic output, and the at least a biological extraction.
 14. Themethod of claim 11, further comprising: generating a third prognosticoutput; and selecting a second prognostic output from the plurality ofsecond prognostic outputs by determining that the second prognosticoutput matches a third prognostic output.
 15. The method of claim 11,further comprising generating the at least a second prognostic output byexecuting a K-nearest neighbors algorithm as a function of the secondtraining set and the at least a first prognostic output.
 16. The systemof claim 11, determining that the at least a second prognostic outputincludes a fundamental prognostic label further comprises identifying aprognostic label of the at least a second prognostic output in afundamental label listing.
 17. The system of claim 16, wherein thefundamental label listing includes an entry by an expert identifying aprognostic label of the at least a second prognostic output as afundamental prognostic label.
 18. The method of claim 11, whereindetermining that the at least a second prognostic output includes afundamental prognostic label further comprises: determining a number ofentries in the second training set identifying a prognostic label of theat least a second prognostic output as caused by a fourth prognosticlabel; and determining that the number of entries fails a thresholdcomparison.
 19. The system of claim 11 further comprising: displayingone or more follow-up suggestions for acquisition of at least a secondbiological extraction at a user output device; and receiving the atleast a second biological extraction;
 20. The system of claim 11 furthercomprising: receive at least a second biological extraction; generatinga third prognostic output as a function of the first training set andthe at least a second biological extraction; determining that a singleprognostic output of the plurality of second prognostic outputscontradicts the third prognostic output; and eliminating the singleprognostic output from the plurality of second prognostic outputs.